The present invention relates generally to transmitting sensor data from a mobile device and more specifically to evaluating sensor data and controlling the transmission of data packets from the mobile device (e.g. a motor vehicle) to a back end processing system based in part on navigation information and network availability.
Existing predictive maintenance systems allow for early determinations of anticipated problems with operational devices. In these systems, product embedded information devices (PEIDs), which may be embodied as sensors, record the various operational aspects of a device. These PEIDs can record various factors, such as oil pressure, fluid levels, operating efficiency, time since previous repairs, locations, and other factors.
An existing predictive maintenance technique is a resident calculation technique in which an on-board computing system analyzes sensor data for the mobile device. For example, the mobile device may be an automobile or piece of heavy construction equipment that may travel to various locations over the course of a day. In addition, the mobile device may also include navigational processing systems, such as a global positioning system (GPS) receiver that coordinates a physical location of the mobile device with a map database providing a visual or audio indication of the mobile device's location. These navigational systems also include planning a route for the mobile device and providing driving directions to the controller of the mobile device.
Due to size and processing limitations, mobile devices do not have the capacity for sophisticated levels of computation as it relates to the events determined by the sensors. These systems can provide basic computing ability, which typically consists of comparing a sensor data reading to a chart of ranges. If the sensor data is outside of the range, the processing device may then provide a cursory notification. For example, if the oil level is below a threshold level, an oil light may be illuminated. These on-board systems are restricted to basic computations of a binary determination of whether a component's operation is either inside or outside of a predetermined operating range.
Another predictive maintenance technique includes using a back end processing system to perform various levels of calculations on the sensor data. This technique is typically limited to stationary devices because there is a dedicated communication path between the device and the back end processing system. It can be beneficial to communicate the data packet between the remote device and the back end processing system, but problems exist in the limited amount of data that can be exchanged therebetween. The back end processing system may be able perform a larger variety of processing operations on this data packet than available with the on-board processing system of the remote device. The back end processing system may also be able to additionally cross reference the sensor data with a large collection of information available in a networked environment, thereby providing a greater degree of analysis than currently locally available on the remote device.
Limitations associated with the remote device communicating with the back end processing system include the remote device's location and ability, as well as costs, to transmit data. The remote device may include the ability to transmit data over different mediums (e.g. WLAN, cellular, Bluetooth, terrestrial, etc.) Each medium includes corresponding factors, such as transmission range, cost and available bandwidth. For example, a WLAN connection may have little cost and a high bandwidth, but a very limited transmission range. Conversely, the terrestrial connection may have extremely high costs, limited bandwidth and an almost global transmission range.
As the mobile device includes the ability to communicate across numerous transmission mediums, it is beneficial to determine which data should be sent over which transmission medium and when the data can be sent. Currently, mobile devices include the ability to collect the sensor data and transmit the data over one of several available transmission mediums. These existing techniques fail to provide for the transmission costs, but rather coordinate data transmission based on transmitting when one of several networks become available. Existing techniques further do not utilize positioning information in making transmission determinations. Based on the varying degrees of transmission mediums, it would be beneficial to efficiently detect and select various transmission techniques as associated with the corresponding event detected by the sensor.